library(immunarch)
library(data.table)
library(gridExtra)
library(plotly)
Ovarian Cancer TRB
#### BLOOD
immdata_ova_trb<-repLoad(OC_path)
Healthy TRB
#### BLOOD
immdata_H_trb<-repLoad(H_path)
Combine OC and H
data <- c(immdata_ova_trb$data, immdata_H_trb$data)
Before subsampling
copy <-data
public_b <- repOverlap(copy, .method = "public", .verbose = F, .col = "aa")
public_b_df = as.data.frame(public_b)
f <- plot_ly(x = colnames(public_b_df), y = rownames(public_b_df), z = as.matrix(public_b_df),
type = "heatmap",
zauto = F, zmin = 0, zmax = 4000)
f <- f %>% layout(title = 'Repertoire Overlap TRB Blood OC and H',
xaxis = list(title = 'Sample', tickangle=-90),
yaxis = list(title = 'Sample'))
f
Low TRB clones
clones_trb<-repExplore(data, .method = "clones")
trb_blood_order = arrange(clones_trb, Clones)
rownames(trb_blood_order) = c()
head(trb_blood_order)
## Sample Clones
## 1 21_TRB_H 1725
## 2 49_TRB_H 7328
## 3 48_TRB_H 17252
## 4 OVA9_TRB 19477
## 5 1_TRB_H 20394
## 6 37_TRB_H 21989
Subsample Omit 21_H, 49_H
data[which(names(data) %in% c("21_TRB_H", "49_TRB_H"))] <- NULL
sub_b = repSample(data, .method = "downsample", .n = 17252)
after subsampling
copy_data_trb <-sub_b
public_b <- repOverlap(copy_data_trb, .method = "public", .verbose = F, .col = "aa") #%>% vis()
public_b_df = as.data.frame(public_b)
f_a <- plot_ly(x = colnames(public_b_df), y = rownames(public_b_df), z = as.matrix(public_b_df),
type = "heatmap",
zauto = F, zmin = 0, zmax = 200)
f_a <- f_a %>% layout(title = 'Repertoire Overlap TRB Blood OC and H',
xaxis = list(title = 'Sample', tickangle=-90),
yaxis = list(title = 'Sample'))
f_a